Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images

• URL: http://arxiv.org/abs/2609.05520v1
• Date: Tue, 01 Sep 2026 05:37:06 GMT
• Title: Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images
• Abstract summary: We tackle automatic anomaly detection in multi-illumination and multi-focus display images.<n>We propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection.<n>Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics.
• Score: 22.28698591377191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are hard to spot out in RGB images and by a model trained with only normal data. To address this, we propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection. In our framework, Multiresolution Knowledge Distillation (MKD) is adopted as a baseline, which operates by measuring feature similarities between the teacher and student networks. Based on MKD, we propose a novel contrastive learning method, namely Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features. Furthermore, we propose the blending module that transforms and aggregate multi-channel information to the three-channel input layer of MCD. Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics on the collected Multi-illumination and Multi-focus display image dataset for Anomaly Detection (MMdAD).

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